Practical Insights on Grasp Strategies for Mobile Manipulation in the Wild

Carolyn Chen; Dan Helmick; Dan Kruse; Isabella Huang; James Borders; JC Hancock; Lukas Kaul; Mark Tjersland; Richard Cheng; Sangwoon Kim

REVIEW 4 major objections 3 minor 1 cited by

Practical Insights on Grasp Strategies for Mobile Manipulation in the Wild

T0 review · 4 major / 3 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read This paper reports that a fully autonomous mobile manipulator, SHOPPER, succeeded in 61 percent of 335 distinct grocery picks in an unmodified store, and attributes every failure to one of eight root causes.

desk verdict The failure analysis is the real contribution, but the taxonomy's percentages rest on unvalidated post-hoc labeling, so treat them as directional, not measured. read the letter →

arxiv 2504.12512 v2 pith:WT245GXY submitted 2025-04-16 cs.RO cs.SYeess.SY

Isabella Huang , Richard Cheng , Sangwoon Kim , Dan Kruse , Carolyn Chen , Lukas Kaul , JC Hancock , Shanmuga Harikumar

show 3 more authors

Mark Tjersland James Borders Dan Helmick

This is my paper · ORCID

classification cs.ROcs.SYeess.SY

keywords mobilemanipulationgraspinggrocerystorefieldtestingfailureanalysissuctiongraspadmittancecontrolreal-worlddeployment

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper reports a large-scale field test of SHOPPER, a mobile manipulator that autonomously retrieves grocery items in an unmodified real store. The authors aim to establish where the bottlenecks actually are for in-the-wild grasping: after 335 distinct pick attempts and about ten hours of runtime, SHOPPER succeeded 61 percent of the time, and every failure was sorted into one of eight root causes. The largest class, at 36 percent, is items that are too heavy, bulky, or otherwise incompatible with the available tools; the paper argues that open-loop execution, missing real-time item state tracking, and immature bimanual coordination are the fundamental gaps. The value of the claim is practical: it turns vague talk about real-world grasping difficulty into a quantified failure distribution that can direct future research.

What carries the argument

The argument is carried by the SHOPPER platform and its grasp-strategy suite operating as one integrated system, plus the eight-cause failure taxonomy that organizes the field-test data. The hardware pairs a two-finger gripper with a custom suction tool; the software pipeline detects and segments items, classifies them into strategy categories, plans grasps with either 2D antipodal hull sampling, 3D ellipsoidal shape-completed sampling, handle keypoint insertion, or side grasps for bags, and executes through compliant admittance control. Three mechanisms do the heaviest lifting: dithering, small compliant probing motions that recovered 14 of 17 initially failed suction grasps; a non-diagonal stiffness coupling in the admittance controller that deflects the tool upward when extraction is blocked by a shelf lip; and runtime tool-pose correction by point-cloud registration that brings commanded gripper poses to sub-centimeter accuracy. The taxonomy R1-R8 is what turns the raw pick attempts into a conclusion: each failed pick is assigned to one root cause, and the resulting distribution is the paper's evidence for where the remaining research effort should go.

What would settle it

Re-annotate the recorded failed picks with an independent panel using a written protocol and check whether the R1-R8 distribution is reproduced; if coders disagree or the top cause changes, the reported percentages are not stable ground truth.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central discovery is a quantified failure taxonomy from a fully integrated robot operating in a real grocery store. SHOPPER attempted 335 distinct picks across three nights and successfully placed the target item in its basket in 61 percent of attempts. The authors attribute all observed failures to eight root causes: R1 heavy, bulky, or incompatible items (36.0 percent); R2 edge cases in motion and grasp planning (15.2 percent); R3 the target item being unexpectedly displaced during execution (14.4 percent); R4 difficult extraction from fixtures (8.8 percent); R5 interference from neighboring items (8.8 percent); R6 the item falling off the tool en route to the basket (7.2 percent); R7 perception errors such as missed collision voxels (5.6 percent); and R8 kinematic imprecision (4.0 percent). The tool-specific breakdown shows that suction failures are dominated by item incompatibility, while gripper failures are dominated by unexpected item displacement. The paper's interpretive claim is that most of these failures trace to open-loop execution without real-time item state tracking, and that the capabilities most needed for general-purpose mobile manipulation are reliable object state tracking and dual-arm coordination.

Load-bearing premise

The failure taxonomy is load-bearing, and it rests on the authors' manual labeling of each failed pick into one of eight causes, with no reported labeling protocol or cross-check, so the percentages could shift under different coders.

Editorial extensions

If this is right

On this item set, heavy, bulky, or otherwise incompatible items are the single largest failure cause (36 percent overall; 45.6 percent of suction failures), so general grocery picking will require bimanual or alternative nonprehensile strategies.
Open-loop grasp execution is a common thread: failures from displaced items, neighbor interference, and drops during transport (R3, R5, and R6) together account for about 30 percent of misses and would need closed-loop state tracking to address.
A compliant dithering search recovers most initial suction misses (14 of 17), so small compliant probing around the planned pose is an effective and cheap robustness mechanism.
Extraction remains hard in narrow shelf openings even with admittance control; adding rotational compliance during extraction is the paper's stated next step.
The full-stack, in-store test format surfaces bottlenecks such as planning edge cases, kinematic imprecision, and partial observability that tabletop grasping benchmarks abstract away.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Extending the paper's data, the non-planning failure classes look addressable by hardware and control changes; if they are additive, eliminating the largest tool-relevant classes could plausibly lift success from 61 percent to the mid-80s on this item set, a projection the paper does not make.
The R1-R8 scheme could be reused as a reporting standard for other mobile-manipulation deployments; if multiple teams published the same breakdown, bottleneck comparisons across platforms would become possible, which the paper leaves implicit.
The paper's own diagnosis suggests a concrete experiment it does not run: adding wrist cameras or tactile sensing to track the item through grasp and transport, then measuring whether R3, R5, and R6 failures drop, would directly test the open-loop-execution claim.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The paper's real contribution is the empirical failure analysis—335 picks in an unmodified grocery store with a breakdown into eight root causes. The grasp strategies themselves are mostly assembled from known pieces and the authors' prior RSS 2023 system, but the field data is new and genuinely useful. The stress-test note is on target: the failure taxonomy is built on the authors' post-hoc labeling of each failed pick, with no coding protocol, no inter-annotator agreement, and no per-pick evidence in the paper. That makes the specific percentages in Table I directional, not measured. The central success-rate claim (61%) is a simple count and I find it credible. The eight-cause list is plausible and matches my intuition about where real-world grasping breaks: heavy/bulky items, open-loop execution, extraction clearance, perception noise. The categories overlap in causal terms—R3, R5, and R6 all stem from lack of real-time item state tracking—so a single failure might plausibly be coded into more than one bucket. The paper would be stronger if it shipped the per-pick annotations, the labeling protocol, and ideally inter-annotator agreement numbers. Without those, the taxonomy is a useful framing but not an objective measurement. I do not see a load-bearing flaw in the system's engineering: the description of the grasp pipeline, the non-diagonal admittance coupling for extraction, and the dithering recovery data (14/17) are all concrete and credible. The paper is honest about excluding glass items for safety; that's a limitation to keep in mind, not a flaw. The citation pattern is fine—the authors cite their prior work for system details, and the empirical result is self-contained. This paper is for robotics practitioners working on mobile manipulation and grasping, especially those doing field deployments. It deserves a serious referee: the data is valuable, the insights are actionable, and the writing is clear. The referee should ask for the annotation protocol, per-pick evidence, and confidence intervals for the percentages. My recommendation is to engage with it—conditional acceptance is appropriate if the authors can show the taxonomy isn't just one team's subjective post-hoc coding.

Referee Report

4 major / 3 minor

Summary. This paper presents SHOPPER, a wheeled dual-arm mobile manipulation platform with suction and gripper tools, and reports on its deployment in an unmodified grocery store. The authors describe a modular pipeline (perception, grasp-strategy classification, grasp pose planning, execution with admittance control) and a set of grasp strategies for shelves, produce piles, handled items, bags, and obstacles. The central empirical result is a field test with 335 distinct pick attempts and a 61% success rate (item placed in basket). The paper's main analytical contribution is a taxonomy of eight root causes (R1-R8) for all observed failures, with the distribution reported in Table I. The authors then draw practical conclusions about bottlenecks such as heavy/bulky items, open-loop execution, lack of real-time item state tracking, difficult extractions, and perception/kinematic noise.

Significance. If the failure taxonomy and success-rate claims are taken at face value, this is a useful systems-level data point for the mobile manipulation community: it quantifies performance in a genuinely unstructured environment, describes concrete engineering mitigations (dithering, admittance control with a non-diagonal stiffness term, tool-pose correction, ICP alignment), and identifies open problems. The paper's strengths include its full-stack online operation in an unmodified store, the relatively large number of pick attempts, the explicit disclosure of excluded glass items, and the public website with per-trial rankings. The main weakness is that the central analytical contribution, the R1-R8 distribution in Table I, rests on a post-hoc manual labeling process that is not described, validated, or made auditable; because the percentages drive the paper's qualitative insights, this is a load-bearing issue rather than a presentation concern.

major comments (4)

[Section VI.A / Table I] The paper reports R1-R8 as "eight fundamental root causes behind all pick failures" and uses Table I to rank their frequencies, but no labeling protocol is provided. There are no explicit definitions of the decision boundary between categories (for example, when a failure is R3 versus R5 versus R6), no statement of whether labels were assigned prospectively or retrospectively by one or multiple coders, no per-pick evidence, and no inter-annotator agreement measure. Since the percentages (R1 at 36.0%, R2 at 15.2%, R3 at 14.4%) drive the conclusions in Section VI.B, please provide a coding protocol with mutually exclusive decision rules, raw counts per category and per tool, and, if possible, per-pick annotations or a reliability check. Without this, the distribution in Table I should be described as an exploratory classification rather than a validated measurement.
[Section VI.B / Table I] Several of the eight categories are causally overlapping as discussed in the text. R3 (unexpectedly displaced target item), R5 (interference from neighboring item), and R6 (fell on the way to basket) are all attributed in Section VI.B to the same underlying deficiency, namely the lack of real-time item state tracking. Similarly, R4 (difficult extract) and R2 (edge cases in planning algorithms) can be hard to separate when shelf geometry restricts the admissible extraction trajectory. As defined, a single observed failure could plausibly be assigned to more than one bucket, so the reported percentages do not necessarily form a partition. Please either define mutually exclusive coding criteria that resolve these overlaps or reframe the table as symptom categories rather than fundamental root causes.
[Section V.B / Section VI.A] The success-rate accounting is ambiguous with respect to recovery actions. Section V.B states that during the latest field test, 14 of 17 instances in which the suction grasp initially failed were successfully recovered through dithering. It is not specified whether these recovered attempts are counted as successes or as failures in the 61% value, nor whether the 17 instances are included in the 335 distinct pick attempts. Please define precisely what counts as a pick attempt, what counts as a success, and how transient failures that are later recovered are recorded in the denominator and in Table I.
[Section VI.A / Table I] Table I presents percentages without raw counts or uncertainty. With 335 picks and a 39% failure rate, the rarest categories correspond to roughly five to ten observed failures (e.g., R8 at 4.0% overall, or R8 at 2.5% for suction), and small changes in coding can move these percentages substantially. Tool-specific comparisons are correspondingly fragile. Please report the raw number of failures per category and per tool, and provide at least the denominators or binomial confidence intervals so that the reader can assess the precision of the ranking.

minor comments (3)

[Abstract / Section IV.A] The abstract says the goal is "picking any item in a real grocery store," but Section IV.A discloses that 236 of 959 automatically detected items are excluded from the graspable set because they are made of glass. Please qualify the "any item" phrasing to match the actual item scope.
[Section V.C] The non-diagonal admittance control term is described only verbally. An equation or a small schematic showing how a deviation along the extraction axis produces motion in the vertical axis would make the mechanism much easier for readers to reproduce.
[Table I] The tool-specific columns do not all sum exactly to 100% (the gripper column sums to approximately 99.9%), presumably due to rounding. Please add a footnote stating that percentages are rounded and indicate the total number of failures per tool.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper reports measured system performance and a failure taxonomy; no prediction is defined in terms of its inputs.

full rationale

The paper's contributions are an implemented grasping pipeline and an empirical field-test analysis. The central quantitative claim — 335 distinct picks with a 61% success rate — is a direct measurement, not the output of a fitted model or a derivation. The eight root causes in Table I are a post-hoc classification of observed failures, not quantities defined by equations that are then 'predicted' back. The design choices (2D antipodal grasping, suction grasping, dithering, admittance control) are engineering strategies supported by external or independently described methods; the citations to prior work by the same authors ([1], [26], [27]) support subsystem details such as mapping, point-cloud registration, and bimanual picking, rather than the paper's empirical claims. The failure percentages rest on manual labeling without a reported inter-annotator protocol, which is a measurement-reliability limitation and not a circularity: the taxonomy is not used to define success, nor is the distribution an input to any calculation that then reproduces itself. No equation in the paper equates a fitted parameter to a reported result, and no load-bearing claim is justified solely by a self-citation. Therefore there is no self-definitional, fitted-input, or self-citation-load-bearing step, and the appropriate score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper's central evaluation rests on the physical robot and the store environment, which are not theoretical constructs. No new physical entities or fitted parameters are introduced. The main extra-mathematical assumptions are that the navigation/localization error bound holds and that the failure labels are reliable.

assumptions (3)

domain assumption Navigation and localization bring the robot to within ±40 cm of each requested item before grasping begins.
Stated in Section IV-B: 'we assume that these modules allow the robot to reach the approximate location of any requested item (within ±40 cm).' If this assumption failed often, failure causes could be misattributed to grasping rather than navigation.
domain assumption The segmented point cloud from a single view is sufficient for grasp planning, with ellipsoid shape completion used only for produce.
Section V-E discusses partial observations and assumes produce is approximately ellipsoid; for shelved items it assumes the 2D projection of the partial point cloud contains the relevant contour for antipodal sampling.
ad hoc to paper The manually labeled failure modes R1-R8 form a reliable partition of all observed pick failures.
Section VI-A introduces R1-R8 without a labeling protocol, inter-annotator agreement, or per-failure evidence. The taxonomy is invented by the authors for this paper and is load-bearing for the central failure analysis.

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Cite this review

Pith. "Pith review of Practical Insights on Grasp Strategies for Mobile Manipulation in the Wild." pith.science (2026). https://pith.science/paper/WT245GXY

@misc{pith2026250412512,
  author       = {Pith},
  title        = {Pith review of: Practical Insights on Grasp Strategies for Mobile Manipulation in the Wild},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WT245GXY}},
  note         = {Machine review of arXiv:2504.12512}
}

read the original abstract

Mobile manipulation robots are continuously advancing, with their grasping capabilities rapidly progressing. However, there are still significant gaps preventing state-of-the-art mobile manipulators from widespread real-world deployments, including their ability to reliably grasp items in unstructured environments. To help bridge this gap, we developed SHOPPER, a mobile manipulation robot platform designed to push the boundaries of reliable and generalizable grasp strategies. We develop these grasp strategies and deploy them in a real-world grocery store -- an exceptionally challenging setting chosen for its vast diversity of manipulable items, fixtures, and layouts. In this work, we present our detailed approach to designing general grasp strategies towards picking any item in a real grocery store. Additionally, we provide an in-depth analysis of our latest real-world field test, discussing key findings related to fundamental failure modes over hundreds of distinct pick attempts. Through our detailed analysis, we aim to offer valuable practical insights and identify key grasping challenges, which can guide the robotics community towards pressing open problems in the field.

Figures

Figures reproduced from arXiv: 2504.12512 by the authors.

**Figure 2.** Overview of our modular, interconnected software [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 4.** Overview of the SHOPPER grasp module. item, the perception module is also responsible for estimating the bounding box of the item, computing a segmented point cloud of the item, and also determining the pose of the item using Robust PCA[21] on a center patch of the item. The perception module has three key components - a detector, a classifier, and a segmenter. The detector, based on DETR [22], is trained on fully s… view at source ↗

Figures from the paper (3 more)

**Figure 5.** Figure 5: Examples of the diverse range of grasp strategies with robot perception visualized at top: (a) a suction grasp that [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]

**Figure 6.** Figure 6: Extraction of a shelf item behind a clear lip. [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]

**Figure 7.** Figure 7: Examples of each failure mode listed in Table I: (a) a [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

From Pixels to Shelf: An Integrated Robotic System for Autonomous Supermarket Stocking with a Mobile Manipulator
cs.RO 2025-09 conditional novelty 4.0 of 10

An integrated mobile manipulator system achieved 98.3% pick-and-place success in 724 mock supermarket stocking events, at 68.2 seconds per item, still slower than human workers.

Reference graph

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.

[1] [1]

Demonstrating Mobile Ma- nipulation in the Wild: A Metrics-Driven Approach

Max Bajracharya et al. “Demonstrating Mobile Ma- nipulation in the Wild: A Metrics-Driven Approach”. In: Robotics Science and Systems . RSS2023. 2023

work page 2023

[2] [2]

Research Challenges and Progress in Robotic Grasping and Manipulation Competitions

Yu Sun et al. “Research Challenges and Progress in Robotic Grasping and Manipulation Competitions”. In: IEEE Robotics and Automation Letters (2022)

work page 2022

[3] [3]

The Hard Lessons of DARPA’s Robotics Challenge

Erico Guizzo and Evan Ackerman. “The Hard Lessons of DARPA’s Robotics Challenge”. In: IEEE Spectrum (2015)

work page 2015

[4] [4]

RoboCup@ Home: Sci- entific Competition and Benchmarking for Domestic Service Robots

Thomas Wisspeintner et al. “RoboCup@ Home: Sci- entific Competition and Benchmarking for Domestic Service Robots”. In: Interaction Studies 10.3 (2009)

work page 2009

[5] [5]

Lessons from the Amazon picking challenge: Four aspects of building robotic systems

Clemens Eppner et al. “Lessons from the Amazon picking challenge: Four aspects of building robotic systems.” In: Robotics: science and systems . 2016

work page 2016

[6] [6]

RoboCup@Work: Competing for the Factory of the Future

Gerhard K. Kraetzschmar et al. “RoboCup@Work: Competing for the Factory of the Future”. In: RoboCup 2014: Robot World Cup XVIII . 2015

work page 2014

[7] [7]

Autonomous Door Opening and Plugging in with a Personal Robot

Wim Meeussen et al. “Autonomous Door Opening and Plugging in with a Personal Robot”. In: IEEE Intl. Conf. on Robotics and Automation . 2010

work page 2010

[8] [8]

Herb 2.0: Lessons Learned From Developing a Mobile Manipulator for the Home

Siddhartha S. Srinivasa et al. “Herb 2.0: Lessons Learned From Developing a Mobile Manipulator for the Home”. In: Proceedings of the IEEE (2012)

work page 2012